1. Project Introduction

The Great Pacific Garbage Patch

The North Pacific Gyre is created by ocean currents connecting two smaller garbage patches: one near Japan and another between Hawaii and California. These currents form a large vortex that spans an area approximately twice the size of Texas, trapping debris within its rotation.

The Great Pacific Garbage Patch, part of this gyre, is not a solid island of trash but a massive area of polluted ocean. It is dominated by microplastics, with concentrations of up to 1.9 million pieces per square mile. Larger items, such as abandoned fishing nets, plastic containers, and buoys, are also present, with pollution stretching over 2,000 miles.

Marine debris has severe consequences for marine life. Sea turtles mistake plastic bags for food, while birds feed plastic fragments to their chicks, causing injury or starvation. Marine mammals are at risk of becoming entangled in discarded fishing nets, often drowning in a process known as ghost fishing.

This debris also disrupts the marine food web. Microplastics and other trash block sunlight needed by plankton and algae, which are crucial to the ocean’s ecosystem. A decline in these organisms reduces food sources for other species, potentially impacting the entire food chain and threatening seafood availability for humans.

Plastics exacerbate these problems by releasing harmful chemicals, such as BPA, as they degrade. They also absorb pollutants like PCBs from seawater, introducing these toxins into the food chain when consumed by marine animals.

Methods of Microplastic Collections

Organizations like The Ocean Cleanup are actively working to remove waste from high-pollution areas such as the Great Pacific Garbage Patch. Their efforts focus on innovative strategies to extract plastic debris and reduce the environmental impact of marine pollution.

To address the vast amounts of trash, multiple methods are employed, each tailored to target different types of debris. Hand picking is used for larger, visible items, such as fishing nets and containers. For microplastics and smaller debris, specialized tools are implemented.

The Neuston net, a fine mesh net traditionally used in oceanography, collects samples from the ocean’s surface. Its improved counterpart, the Manta net, allows for continuous-flow collection, increasing efficiency. Grab sampling involves collecting known volumes of surface water using glass containers, ensuring all microplastics within the sample are captured. For debris embedded in coastal sands, a PVC cylinder is utilized to extract sand samples for analysis and cleanup.

By combining these approaches, organizations are making strides in tackling marine pollution while contributing to scientific understanding of its composition and distribution.

Rationale to Study Plastic Collection in the Garbage Patch

Studying plastic collection in the Great Pacific Garbage Patch is essential for driving progress in cleanup efforts and addressing marine pollution effectively. Quantifying the amount of plastic removed provides a clear measure of success, offering tangible evidence of progress toward cleanup goals. Analyzing this data also helps evaluate the effectiveness of different methods, identifying which strategies work best and why.

Furthermore, the information gathered plays a critical role in shaping global policies. Reliable data supports legislative efforts aimed at improving waste management and reducing plastic production. It also raises public awareness by highlighting the scale of the problem, inspiring greater support for environmental initiatives.

Finally, studying plastic collection fosters continuous improvement. Feedback from data collection enables the optimization of technologies and methodologies, making future cleanup operations more efficient and impactful. This research not only contributes to the current fight against marine pollution but also lays the groundwork for more sustainable solutions.

2. Research Question

3. Dataset Information

Table 1. Dataset information
Detail Description
Data Source NOAA NCEI Marine Microplastics
Retrieved from https://www.ncei.noaa.gov/products/microplastics
Variables, wrangled Year, Month, Day, Measurement, Unit, Density.Range, Density.Class, Latitude, Longitude
Date Range, wrangled April 28, 1972 - February 21, 2014
Table 2. NOAA NCEI Microplastics data contributors
Count Citation DOI
1 Barrows et al.2018 https://doi.org/10.1016/j.envpol.2018.02.062
3 Faure et al.2015 https://doi.org/10.1007/s11356-015-4453-3
5 Law et al.2014 https://doi.org/10.1021/es4053076
6 Goldstein et al.2013 https://doi.org/10.1371/journal.pone.0080020
7 Courtene-Jones et al. 2020 https://doi.org/10.1016/j.marpolbul.2020.111092
8 Russell and Webster, 2021 https://doi.org/10.1016/j.marpolbul.2021.112210
9 Law et al.2010 https://doi.org/10.1126/science.1192321
21 Tunnell et al. 2020 https://doi.org/10.1016/j.marpolbul.2019.110794
31 Kanhai et al. 2017 http://dx.doi.org/10.1016/j.marpolbul.2016.12.025
38 Alvarez-Zeferino et al. 2020 https://doi.org/10.1016/j.resconrec.2019.104633
48 McEachern et al. 2019 https://doi.org/10.1016/j.marpolbul.2019.07.068
57 Queiroz et al.2022 https://doi.org/10.1016/j.scitotenv.2022.156259
63 Suaria et al.2020 https://doi.org/10.1016/j.envint.2020.105494
82 Pedrotti et al.2022 http://dx.doi.org/10.1016/j.scitotenv.2022.155958
133 Eriksen et al.2014 https://doi.org/10.1371/journal.pone.0111913
138 de Haan et al.2019 https://doi.org/10.1016/j.marpolbul.2019.01.053
142 Nash et al. 2023 https://doi.org/10.1021/acs.est.2c05926
145 Fulfer and Walsh, 2023 https://doi.org/10.1038/s41598-023-36228-8
176 Eriksen et al.2018 https://doi.org/10.1016/j.envpol.2017.09.058
177 Alfaro-Núñez et al.2021 https://doi.org/10.1038/s41598-021-85939-3
183 de Haan et al.2022 https://doi.org/10.1088/1748-9326/ac5df1
189 Joyce et al. 2022 https://doi.org/10.1016/j.scitotenv.2022.154036
223 Seemungal et al. 2022 https://doi.org/10.1007/s11852-021-00846-z
249 González-Hernández et al. 2020 https://doi.org/10.1016/j.marpolbul.2019.110757
291 Eriksen et al.2013 https://doi.org/10.1016/j.marpolbul.2012.12.021
296 Egger et al.2022 https://doi.org/10.1038/s41598-022-17742-7
300 Egger et al.2020 https://doi.org/10.1088/1748-9326/abbb4f
311 Tanhua et al.2020 https://doi.org/10.1371/journal.pone.0243203
314 Rodrigues et al. 2020 https://doi.org/10.3389/fenvs.2020.582217
414 Egger et al.2021 https://doi.org/10.3389/fmars.2021.626026
427 Rodrigues et al. 2022 https://doi.org/10.3389/fenvs.2022.998513
474 Setiti et al.2021 http://dx.doi.org/10.12681/mms.24899
569 Cruz-Salas et al. 2022 https://doi.org/10.1016/j.rsma.2022.102423
600 Rose and Webber, 2019 https://doi.org/10.1016/j.scitotenv.2019.01.319
2433 Kanhai et al. 2018 https://doi.org/10.1016/j.marpolbul.2018.03.011
2581 Egger et al.2020 https://doi.org/10.1038/s41598-020-64465-8
3239 Suaria et al.2016 https://doi.org/10.1038/srep37551

4. Exploratory Analysis

4.1 Global Data

This is data from entire globe.

4.2 NPSG Data

Distribution of Samples

Samples within the NPSG.

Distribution of Plastics Densities

Samples of different density classes.

Other variables: does the amount of plastic differ with time?

Plastics over Time

Plastics over Time

At first glance, there appears to be an upward trend.

5. Analysis

5.1 Variability in sampling methods

Table 2. Occurrences of Various Sampling Methods within Global Dataset
Method Number of Samples
Aluminum bucket 57
AVANI net 18
CTD rosette sampler 6
Day grab 17
Grab sample 1116
Hand picking 5810
Intake seawater pump 131
Manta net 861
Megacorer 90
Metal spoon 76
Neuston net 7207
Petite Ponar benthic grab 36
Plankton net 74
PVC cylinder 370
Remotely operated vehicle 40
Shipek grab sampler 9
Stainless steel spoon 50
Van Dorn sampler 181
Van Veen grab sampler 7
Sampling Method

Sampling Method

Measurements take by each sampling method.

5.2 Plastics and time

Table 3. Generalized Linear Model Analysis
Analysis P-Value
Linear Regression 1.284e-05
Table 4. AIC Analysis, formula = Measurement ~ Year + Month + Latitude + Longitude
Variable AIC
- Longitude 1069.2
no change 1070.6
- Latitude 1071.3
- Month 1076.2
- Year 1090.5

P value is less than significance level of 0.05. AIC suggests Month, Year, and Latitude are explanatory variables.

5.3 Time series

Figure 3. Decomposition of time series

Figure 3. Decomposition of time series

Seasonality removed

Seasonality removed

Table 5. Trend Analyses
Analysis 2-sided P-Value
Seasonal Mann-Kendall 0.11049
Non-Seasonal Mann-Kendall 0.069721

Interpolated values across the years. Time series analysis, there does seem to be seasonality. The trend has a stronger increase in later years.

6. Summary and Conclusions

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